Several established risk factors such as age, parity, family history and body mass index (BMl) contribute to the genesis of invasive breast cancers, the most common non-keratinocyte cancer among women in the United States. The current model of breast cancer indicates progression from normal breast to a benign lesion, noninvasive disease, and then an invasive tumor. Relationships between established breast cancer risk factors and the epigenetic character of breast tumors have begun to be described, and epigenetic alterations including DNA methylation alterations are known early contributors to breast tumorigenesis. However, the presence of epigenetic alterations that occur in the context of invasive breast cancer is underexplored along the continuum of normal breast, pre-neoplastic lesions, and non-invasive breast cancers. Our published preliminary data indicate significant, independent associations of breast tumor DNA methylation profiles with modifiable breast cancer risk factors. Here, we propose to test the hypothesis that DNA methylation variation in normal breast, pre-neoplastic lesions, and non-invasive cancers is associated with disease risk factors and progression to invasive disease. This work will leverage existing tissue and patient data resources from women enrolled in the New Hampshire Mammography Network allowing investigation of DNA methylation in benign lesions and pre-invasive disease. In addition, we will study women of child-bearing age through the New Hampshire Birth Cohort Study, collecting non-neoplastic cells from breast milk and nipple aspirate fluid to evaluate the relationships between DNA methylation and risk factors for breast cancer. Further, we will explore intraindividual changes in DNA methylation from repeated collections of breast milk and nipple aspirate fluid in a subset of women. The goal of this work is to extend our understanding of the biological mechanisms through which established breast cancer risk factors contribute to carcinogenesis, which has broad potential to impact all individuals at risk for breast cancer by better directing existing risk prediction models, and informing novel strategies for breast cancer prevention.